KR101497270B1 - Method and System of Statistical Modeling of Radio Signal Received from Multiple Concurrent Wireless Transmission - Google Patents

Abstract

Disclosed are a method and a system for the statistical modeling of a radio signal received from the simultaneous transmission of wireless transmitters. A ring-shaped region part is formed in a transmitter distribution region with the outermost circle having the maximum distance affected by the signal of a transmitter around a receiver as radius and the innermost circle having the minimum separation distance between the transmitter and the receiver as radius. The present invention includes the steps of: setting the radius of the outermost circle, the radius of the innermost circle, and the number of the transmitters; calculating the radius of the circles to divide the region part into an external part and an internal part; calculating a probability density function of an accumulation signal received from the transmitters which are located in the internal region and the external region; and displaying the signal with the weighted sum modeling of a log-normal distribution using the probability density function.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and system for statistical modeling of radio wave signals received from simultaneous transmission of radio transmitters,

The present invention relates to a method and system for statistical modeling of radio signals received from simultaneous transmission of radio transmitters. More particularly, the present invention relates to a method for accurate statistical modeling of cumulative signals due to simultaneous transmission of multiple radio transmitters in a crowded wireless system.

In recent years, due to the evolution of wireless and mobile communication services, wireless transmitters are distributed densely and the transmission frequency of transmitters is increased, so that inter-signal interference due to the simultaneous transmission of a plurality of wireless transmitters can greatly affect the performance of the wireless system. In general, since the maximum power delivery amount in one radio transmitter is fixed, the interference problem is not considered much by adjusting the maximum power delivery amount in consideration of the interference of the transmitter.

However, when a plurality of radio transmitters transmit simultaneously, large power interference may occur because the transmitted signals are weighted at the receiver. Since the total transmittance is proportional to the number of simultaneously transmitting radio transmitters, It is difficult to predict the degree of interference.

As such, the statistical characteristics of the signal can be analyzed in a cognitive radio or a cellular mobile communication system because of the large influence of the accumulated signals or interference from a plurality of radio transmitters. The first method can analyze and model the statistical characteristics of the cumulative reception interference according to the protocol in the power control and MAC (Medium Access Control) layer in the cognitive radio network. The second method can analyze the performance of the system using statistical modeling of cumulative reception interference in TDMA and CDMA systems.

Statistical analysis of cumulative signals due to simultaneous transmission from wireless transmitters has been of high interest as evolving into a dense radio environment, but in a particular statistical model of conventional cumulative signals, simultaneous transmission from multiple radio transmitters around the radio receiver Due to the complexity of the calculation due to the statistical characteristics of the received signal, it is not possible to obtain the exact form of the equation. As a result, most of them are approximated by log normal distribution, but a very large error may occur. Therefore, a new statistical model that can solve this problem is required.

The present invention provides a method for accurately statistically modeling an accumulated signal due to simultaneous transmission of a plurality of wireless transmitters in a wireless system in which a plurality of wireless transmitters are concentrated, And to provide a method and system for statistical modeling of received radio signals from simultaneous transmission of radio transmitters.

According to an aspect of the present invention, there is provided a method of statistically modeling a radio wave signal received from simultaneous transmission of radio transmitters according to the present invention, the method comprising the steps of: generating an outermost circle having a radius of a maximum distance, Setting a radius of the outermost circle, a radius of the innermost circle, and the number of transmitters in a transmitter distribution area where the innermost circle having the minimum distance between the receivers as a radius forms a ring-shaped area, ; Obtaining a radius of the circle dividing the region into an outer region and an inner region; Obtaining a probability density function of the cumulative signal received from the transmitters existing in each of the outer region section and the inner region section; And representing the weighted sum modeling of the lognormal distribution using the probability density function.

Wherein the step of obtaining the probability density function of the cumulative signal received from the transmitters existing in each of the outer region section and the inner region section comprises: And performing modeling.

Wherein the step of obtaining the radius of the inner circle dividing the region region into the outer region region and the inner region region includes obtaining the radius? Of the inner circle by using the following equation,

는 상기 최내곽 원의 반지름,

는 상기 최외곽 원의 반지름이며, m은 상기 송신기들의 수이고, P₀= 0.85~0.95로 설정할 수 있다.here,

The radius of the innermost circle,

Is the radius of the outermost circle, m is the number of transmitters, and P ₀ = 0.85 to 0.95.

개의 송신기들이 상기 영역부 내에서 발생할 때,

개가 상기 내부 영역부에 존재하고

개가 상기 외부 영역부 내에 존재할 확률을 이항 분포에 의해 다음 식을 이용하여 구하는 단계를 포함하고, Obtaining a probability density function of an accumulated signal received from the transmitters existing in each of the outer region section and the inner region section,

When < RTI ID = 0.0 > transmitters < / RTI >

Is present in the inner region portion

By using a binomial distribution, using the following equation: < EMI ID =

(

)

(

)

는 상기 최내곽 원의 반지름,

는 상기 최외곽 원의 반지름, 그리고

는 상기 내부 원의 반지름을 나타내며, m은 상기 송신기들의 수, i는 상기 내부 영역부에 존재하는 상기 송신기들의 수, 그리고 m-i는 상기 외부 영역부에 존재하는 상기 송신기들의 수이고,

은 상기 영역부에 생성되는 하나의 상기 송신기가 상기 내부 영역부에 존재할 확률이며,

은 상기 영역부에 생성되는 하나의 상기 송신기가 상기 외부 영역부에 존재할 확률이고,

인 것을 특징으로 할 수 있다.here,

The radius of the innermost circle,

The radius of the outermost circle, and

Where m is the number of transmitters, i is the number of transmitters in the inner region, and mi is the number of transmitters in the outer region,

Is a probability that one of the transmitters generated in the area unit exists in the inner area unit,

Is a probability that one of the transmitters generated in the area unit exists in the outer area unit,

.

값으로 설정할 수 있으며,Obtaining a probability density function of the cumulative signal received from the transmitters existing in each of the outer region section and the inner region section,

Value,

는 근사화 정밀도 요구에 따라 적어도 0.01과 0.001 중 하나의 값으로 설정하여,

는 2 ~ 4의 정수 값을 가질 수 있다.here,

Is set to at least one of 0.01 and 0.001 according to the approximation accuracy requirement,

May have an integer value of 2 to 4.

The weighted sum modeling of the log normal distribution using the probability density function may include weighted sum modeling of the log normal distribution using a probability density function of the inner region and a probability density function of the outer region, Lt; / RTI >

)과 분산(

)을 계산하여, 상기 로그 정규 분포의 가중합을 다음 식을 이용하여 구하는 단계이고,The step represented by the weighted sum modeling of the lognormal distribution using the above probability density function may be expressed as:

) And variance (

), And calculating a weighted sum of the lognormal distributions using the following equation,

인 것이 가능하다. here,

Lt; / RTI >

According to another aspect of the present invention, there is provided a statistical modeling system of a radio wave signal received from simultaneous transmission of radio transmitters proposed by the present invention. The system includes an outermost circle whose radius is a maximum distance that a signal of a transmitter affects, And setting the number of transmitters in the transmitter distribution area where the innermost circle having the minimum distance between the receivers as a radius is formed in a ring-shaped area, the radius of the outermost circle, the radius of the circle of the innermost circle, part; An area dividing section dividing the area section into an inner circle existing in the area section and dividing the area section into an outer area section including the outermost circle and the inner circle and an inner area section including the innermost circle circle and the inner circle; A calculation unit for obtaining a probability density function of the cumulative signal received from the transmitters existing in each of the outer area unit and the inner area unit; And a weighted sum modeling unit that represents the weighted sum modeling of the lognormal distribution using the probability density function.

The calculation unit may perform modeling of a log normal distribution of accumulated signals in each of the outer region and the inner region.

The radius (?) Of the inner circle in the region dividing section is obtained using the following equation,

는 상기 최내곽 원의 반지름,

는 상기 최외곽 원의 반지름이며,here,

The radius of the innermost circle,

Is the radius of the outermost circle,

m is the number of the transmitters, and it is possible to set P ₀ = 0.85 to 0.95.

개의 송신기들이 상기 영역부 내에서 발생할 때,

개가 상기 내부 영역부에 존재하고

개가 상기 외부 영역부 내에 존재할 확률을 이항 분포에 의해 다음 식을 이용하여 구하고, The calculation unit

When < RTI ID = 0.0 > transmitters < / RTI >

Is present in the inner region portion

The probability that the dog exists in the outer region is obtained by the binomial distribution using the following equation,

(

)

(

)

는 상기 최내곽 원의 반지름,

는 상기 최외곽 원의 반지름, 그리고

는 상기 내부 원의 반지름을 나타내며, m은 상기 송신기들의 수, i는 상기 내부 영역부에 존재하는 상기 송신기들의 수, 그리고 m-i는 상기 외부 영역부에 존재하는 상기 송신기들의 수이고,

은 상기 영역부에 생성되는 하나의 상기 송신기가 상기 내부 영역부에 존재할 확률이며,

은 상기 영역부에 생성되는 하나의 상기 송신기가 상기 외부 영역부에 존재할 확률이고,

인 것이 가능하다.here,

The radius of the innermost circle,

The radius of the outermost circle, and

Where m is the number of transmitters, i is the number of transmitters in the inner region, and mi is the number of transmitters in the outer region,

Is a probability that one of the transmitters generated in the area unit exists in the inner area unit,

Is a probability that one of the transmitters generated in the area unit exists in the outer area unit,

Lt; / RTI >

According to embodiments of the present invention, a method of accurately statistically modeling an accumulated signal due to the simultaneous transmission of a plurality of wireless transmitters in a wireless system in which a plurality of wireless transmitters are concentrated can be provided to predict a characteristic of a signal, A method and system for statistical modeling of a received radio signal from simultaneous transmission of radio transmitters that can be provided.

로 영역의 이분하는 것을 나타낸 도이다.
도 3은 본 발명의 일 실시예에 따른 무선 송신기들의 동시 전송으로부터 수신된 전파 신호의 통계적 모델링 방법을 나타낸 흐름도이다.
도 4는 본 발명의 일 실시예에 따른 누적 신호의 통계적 모델의 성능 비교를 나타낸 도이다.FIG. 1 illustrates a distribution range of simultaneous transmission wireless transmitters according to an embodiment of the present invention. Referring to FIG.
FIG. 2 is a block diagram of a transmitter according to an embodiment of the present invention,

And Fig.
3 is a flow chart illustrating a method for statistical modeling of a received radio signal from simultaneous transmission of radio transmitters in accordance with an embodiment of the present invention.
FIG. 4 illustrates a comparison of the performance of statistical models of cumulative signals according to an embodiment of the present invention.

The present invention relates to a method and system for statistical modeling of a radio signal received from the simultaneous transmission of radio transmitters and to a method for accurately statistically modeling the cumulative signal due to the simultaneous transmission of several radio transmitters in a crowded radio system .

This is a statistical processing method of signals that can be applied in a wireless network, and can be utilized in various wireless networks such as a cellular mobile communication network, an ad hoc network, a cognitive radio network, and the like.

Generally, when setting the area where a plurality of radio transmitters are distributed, the outermost circle whose radius is the maximum distance that the signal of the transmitter influences around the receiver, and the innermost circle whose radius is the minimum distance between the transmitter and the receiver, Consider ring-shaped areas between circles. In this case, in the model of the cumulative signal using the conventional logarithmic normal distribution, it is understood that as the ratio of the radii of the outermost circle and the innermost circle increases, the error caused by the approximation gradually increases. In the present invention, A statistical model showing good performance regardless of the shape of the distribution area of the transmitter can be obtained by approximating by dividing it into two regions with an arbitrary radius.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 illustrates a distribution range of simultaneous transmission wireless transmitters according to an embodiment of the present invention. Referring to FIG.

에서

까지의 고리 형태의 영역 내에 균일하게 분포하는 것으로 보통 가정된다. 여기서,

은 송신기(20)와 수신기(10) 사이의 최소 이격 거리,

는 무선 송신기(20)로부터 수신기(10)까지 신호가 영향을 줄 수 있는 최대 거리이다. 그리고, 이 영역부를

로 나타내고, 이 때의 무선 송신기(20)들의 개수는

으로 나타낸다. Referring to FIG. 1, when a plurality of wireless transmitters 20 transmit around a specific receiver 10, the distribution of the transmitters X and 20 is radiused around the receiver Rx 10

in

It is usually assumed to be uniformly distributed in the ring-shaped region from the top to the bottom. here,

The minimum separation distance between the transmitter 20 and the receiver 10,

Is the maximum distance a signal from the wireless transmitter 20 to the receiver 10 can affect. Then,

, And the number of radio transmitters 20 at this time is represented by

Respectively.

를 경로 손실 상수,

를 경로 손실 지수,

를 두 지점 사이의 거리로 놓을 경우

로 나타낼 수 있고, 쉐도잉은 로그 정규 분포 모델을 가장 많이 사용한다. 이 때, 도면 1의 수신기(Rx, 10)에서

내의 무선 송신기(20)들의 전송으로부터 수신된 총 누적 신호의 전력량

는 [수 1]과 같이 나타낼 수 있다.Also, the radio propagation channel is a composite channel of path loss and shadowing, which is considered the most generalized channel environment in a wireless system. Here, the path loss is

The path loss constant,

The path loss index,

At a distance between two points

, And shadowing uses the log-normal distribution model most often. At this time, the receiver (Rx, 10)

Of the total cumulative signal received from the transmission of the wireless transmitters (20)

Can be expressed as [Formula 1].

은 송신기들의 개수이고,

는

번째 송신기의 송신 전력,

는

번째 송신기와 수신기와의 거리,

는

번째 송신기와 수신기 사이의 쉐도잉으로 인한 채널 이득으로써 모든 송신기들에 대해 (for all

)평균이 0, 분산이

인 로그 정규 분포로 같은 분포를 갖는다. here,

Is the number of transmitters,

The

Lt; th > transmitter,

The

The distance between the transmitter and the receiver,

The

Lt; th > transmitter as a channel gain due to shadowing between all transmitters

) Average is 0, variance is

The logarithmic normal distribution has the same distribution.

The distribution of I, which is the cumulative signal from the surrounding transmitters 20, is modeled as a lognormal distribution, where the equation of the lognormal distribution can be expressed as:

,

이라 할 때

,

를 [수 3]과 [수 4]와 같이 표현될 수 있다.here,

,

When you say

,

Can be expressed as [Numerical 3] and [Numerical 4].

,

로 나타낼 수 있다.here,

,

.

,

로 쉽게 계산할 수 있기 때문에, [수 3]과 [수 4]에서 얻은 1, 2차 모멘트인

,

와 각각 매칭을 시키고,

,

에 관해 [수 5]와 같이 나타낼 수 있다. The first and second moments of [Formula 2] are

,

, The first and second moments obtained in [Numerical 3] and [Numerical 4]

,

Respectively,

,

Can be expressed as [Numerical Formula 5].

)과, 분산(

)을 얻을 수 있다. As a result, the average (

), And dispersion (

) Can be obtained.

의 값이 커질수록 오차가 증가함을 확인할 수 있다. 다시 말하면, 다수의 송신기(20)들로부터의 누적 신호를 구하는 경우, 송신기(20)들간의 최소 거리로 정의할 수 있는

은 1 m 이하로 매우 작고, 송신기(20)들 간의 신호가 미치는 최대 거리

는 핫스팟의 반경으로서 보통 수십 미터에 이르기 때문에

의 큰 값으로 인해 기존의 로그 정규모델링은 잘 맞지 않는다.
Log normal modeling for existing cumulative signals is shown in Figure 1

The larger the value of the error, the greater the error. In other words, when obtaining the cumulative signal from a plurality of transmitters 20, it is possible to determine the minimum distance between the transmitters 20

Is very small, less than 1 m, and the maximum distance between the signals between the transmitters 20

Is the radius of the hotspot, usually tens of meters

The conventional log normal modeling does not fit well.

로 영역의 이분하는 것을 나타낸 도이다.FIG. 2 is a block diagram of a transmitter according to an embodiment of the present invention,

And Fig.

에서

까지의 고리 형태의 영역 내에 균일하게 분포하는 것으로 보통 가정된다. 여기서,

은 송신기와 수신기(10) 사이의 최소 이격 거리,

는 무선 송신기로부터 수신기(10)까지 신호가 영향을 줄 수 있는 최대 거리이다. 그리고, 이 영역부를

로 나타내고, 이 때의 무선 송신기들의 개수는

으로 나타낸다. Referring to FIG. 2, when a plurality of wireless transmitters are transmitted around a specific receiver receiver 10, the distribution of the transmitters X has a radius centered at the receiver (Rx, 10)

in

It is usually assumed to be uniformly distributed in the ring-shaped region from the top to the bottom. here,

The minimum separation distance between the transmitter and the receiver 10,

Is the maximum distance that the signal from the wireless transmitter to the receiver 10 can affect. Then,

, And the number of radio transmitters at this time is

Respectively.

보다 크고

보다는 작은 임의의 값

를 반지름으로 하는 영역을 점선과 같이 정하여, 상기 영역부인

을 내부 영역부인

와 외부 영역부인

의 두 영역부로 나눌 수 있다.
In the region,

Bigger

A smaller arbitrary value

Is set to be a radial line as shown by a dotted line,

Lt; / RTI >

And the outer domain denomination

And the like.

3 is a flow chart illustrating a method for statistical modeling of a received radio signal from simultaneous transmission of radio transmitters in accordance with an embodiment of the present invention.

로 나타내며, 내부 영역부는

로 나타내고, 외부 영역부는

로 표기하여 나타낼 수 있다.Below,

, And the inner region portion

And the outer region portion is represented by

As shown in FIG.

In step 110, first, an outermost circle whose radius is the maximum distance at which a signal of the transmitter is affected by the receiver, and an outermost circle whose radius is the minimum distance between the transmitter and the receiver is in the form of a ring The transmitter distribution region forming the region portion of the transmitter is formed.

In the transmitter distribution area, the radius of the outermost circle, the radius of the innermost circle, and the number of transmitters can be set.

Then, in step 120, the radius of the circle dividing the region into the outer region and the inner region can be obtained. A detailed description thereof will be given below.

The probability density function of the cumulative signal received from the transmitters existing in each of the outer region section and the inner region section can be obtained in step 130.

) 내에서 생성되는 하나의 송신기가 내부 영역부(

)내에 존재할 확률

과, 외부 영역부(

) 내에 존재할 확률은

는 두 넓이의 비로서 [수 6]과 같이 얻을 수 있다.First,

One transmitter is generated in the inner region section < RTI ID = 0.0 >

) Of the probability

And an outer region (

) Is the probability

Is the ratio of the two dimensions, and can be obtained as [6].

개의 송신기들이 영역부(

) 내에서 발생할 때,

개가 내부 영역부(

) 내에 존재하고

개가 외부 영역부(

) 내에 존재할 확률은 이항 분포에 의해 [수 7]과 같이 표현할 수 있다.In this case,

Lt; RTI ID = 0.0 >

),

(

) And

When the dog is outside the area

) Can be expressed as [7] by the binomial distribution.

이고,

개가 내부 영역부(

) 내에 존재하며,

개가 외부 영역부(

) 내에 존재할 때, 총

개의 송신기들로부터 수신되는 신호의 확률 밀도 함수(Probability Density Function: PDF)를

라고 정의하면 누적 신호의 확률 밀도 함수는

로 나타낼 수 있다. here,

ego,

(

Lt; / RTI >

When the dog is outside the area

), The total

Probability Density Function (PDF) of the signal received from the transmitter

, The probability density function of the cumulative signal is

.

This can be expressed as [Numerical Formula 8].

는 B의 공간 영역 내에 A 개의 무선 송신기들이 균일하게 분포할 때 중심에 있는 수신기에서 수신된 누적 신호의 확률 밀도 함수를 나타낸다. here,

Represents the probability density function of the cumulative signal received at the receiver at the center when A radio transmitters are uniformly distributed within the spatial domain of B.

)에서 임의로 나눈 두 영역

와

에 관해

는 내부 영역부인

내에 존재하는 무선 송신기들로부터 수신된 누적 신호의 확률 밀도 함수이고,

는 외부 영역인

내에 존재하는 무선 송신기들로부터 수신된 누적 신호의 확률 밀도 함수를 나타낸다. Therefore, the area of the transmitter,

) ≪ / RTI >

Wow

About

Lt; / RTI >

Is a probability density function of the cumulative signal received from radio transmitters present in the transmitter,

Lt; / RTI >

Denote the probability density function of the cumulative signal received from the wireless transmitters present in the receiver.

와

을 각각 누적 신호에 대한 로그 정규 모델링의 방법을 적용하여 로그 정규 모델링을 수행할 수 있다. 송신기 분포의 전체 영역인 영역부(

)에서는

의 값이 커서 로그 정규 모델이 큰 오차를 나타내지만, 이 영역을 두 영역으로 나눔으로써 각 영역에서의 내, 외부 반지름의 비율인

,

는 처음의

에 비해 크게 줄어든 값이므로 각 세부 영역에서 로그 정규 모델을 적용할 경우 오차가 거의 없이 매우 잘 맞음을 확인할 수 있다. At this time, the probability density function of the cumulative signal in each region

Wow

Log normal modeling can be performed by applying the log normal modeling method to the accumulated signals. The total area of the transmitter distribution,

)

The log normal model shows a large error, but by dividing this region into two regions, the ratio of the inner and outer radii in each region

,

First

, It can be confirmed that the log normal model is applied very well in each detail area with almost no error.

의 설정 과정은 뒤에서 상세히 설명하기로 한다.For reference,

Will be described later in detail.

At step 140, the weighted sum modeling of the lognormal distribution can be represented using the probability density function.

와

에 각각 로그 정규 모델링을 적용할 수 있고, 이 때, 두 로그 정규 분포의 콘볼루션인

은 Fenton-Wilkinson 방법을 이용하여 또 다른 로그 정규 분포를 따르는

로 나타낼 수 있기 때문에 [수 8]에서의

는 [수 9]와 같이 로그 정규 분포의 가중합으로 나타낼 수 있다.

Wow

Log normal modeling can be applied to each of the two log normal distributions,

Using the Fenton-Wilkinson method,

Can be expressed as [Equation 8]

Can be expressed as a weighted sum of lognormal distributions as in [9].

,

,

이고,

이다. here,

,

,

ego,

to be.

,

은 [수 3], [수 4], 및 [수 5]를 이용한

의 1, 2차 모멘트로부터,

,

은

의 1, 2차 모멘트로부터 얻고,

일 때의

,

은

과

의 1, 2차 모멘트로부터 [수 10]과 같이 나타낼 수 있다.At this time,

,

Is obtained by using [Numerical 3], [Numerical 4], and [Numerical 5]

From the first and second moments of

,

silver

From the first and second moments of

When

,

silver

and

Can be expressed as [Numerical Expression 10] from the first and second moments of the moment.

의 1, 2차 모멘트를 각각

,

라고 하고, here,

The first and second moments of

,

And,

의 1, 2차 모멘트를 각각

,

라고 할 때,

,

로 나타낼 수 있다.

The first and second moments of

,

When you say,

,

.

의 상계(upper bound)에 대한 유도를 통해 항의 개수를 크게 줄일 수 있다.In [9], the following

The number of terms can be greatly reduced through induction to the upper bound of.

의 상계를

로 놓는다면,

가 되어 공비인

가 작을수록

는

의 증가에 따라 급격히 작아지는 등비수열의 형태가 된다. 그러나, 상계

를 작게 만들기 위해서는

의 값이 작아야 하므로

를 1에 가깝게 설정할 필요가 있다. here,

The offset of

If you leave it as,

And the public

The smaller

The

Which is rapidly decreasing with the increase of the ratio. However,

To make small

The value of

Must be set close to 1.

가 0.9인 경우

는

부터는 0.1 이하가 되어 0.1로만 두더라도

,

,

, …로

의 값이 급격히 작아짐을 확인할 수 있다. 이렇게

를 1에 가깝게 설정함으로써

는

가 증가할수록 급격하게 줄어들어 소수의

개의

(

, …,

)에 관한 항들만 가지고도

를 거의 정확하게 나타낼 수 있다. E.g,

Is 0.9

The

It becomes 0.1 or less and it is only 0.1

,

,

, ... in

The value of < / RTI > like this

By setting it close to 1

The

As the number of

doggy

(

, ... ,

), But only with respect to

Can be represented almost accurately.

는 다음의 부등식 [수 11]로 나타낼 수 있다.At this time,

Can be expressed by the following inequality [11].

값으로 설정할 수 있는데,

는 근사화 정밀도 요구에 따라 0.01이나 0.001 등의 작은 값으로 설정할 수 있다. 이에 의해,

는 2 ~ 4의 정수 값을 가진다.
Therefore, the smallest

Value,

Can be set to a small value such as 0.01 or 0.001 depending on the approximation accuracy requirement. As a result,

Has an integer value of 2 to 4.

가 설정되면

이기 때문에, [수 12]에 의해

를 표현할 수 있다.On the other hand, in step 120,

Is set

Because of this, by [Number 12]

Can be expressed.

,

,

는 이미 공간 분포 상에 결정된 값이므로 결국

와

가 서로 설정의 직접적 연관이 있다. 앞에서 언급한 바와 같이,

를 1에 가깝게 설정할수록

의 상계가 작아져 [수 9]에서 더 적은 수의 항들을 사용할 수 있으나,

를 1에 가깝게 설정할수록

는

에 가까워지고,

가

에 너무 가까워지면

와

의 비율 설정에서 비대칭적으로 한 쪽은 작아지고, 나머지는 커지므로 무조건

를 1에 가깝게 설정하는 것도 좋지 않음을 확인할 수 있다. here,

,

,

Is already a value determined on the spatial distribution,

Wow

There is a direct connection between settings. As mentioned above,

The closer to 1

The smaller the upper bound of [n], the fewer terms can be used in [9]

The closer to 1

The

Lt; / RTI >

end

If you get too close to

Wow

Asymmetrically, one side is smaller and the other is larger,

Is set to be close to 1 is not preferable.

로 설정되는 경우

와

의 비율 및 [수 9]에서 항의 개수가 적절하다고 판단하여

로 설정하기로 한다. 물론, 0.85나 0.95 등의 유사한 값들로 조정할 수 있으며, 그 결과에 큰 차이는 없다.
The experimental results in the present invention

Is set to "

Wow

And the number of terms in [Number 9] is appropriate

. Of course, it can be adjusted to similar values such as 0.85 or 0.95, and there is no significant difference in the results.

개의 항을 가지고 [수 13]과 같이 로그 정규 분포의 가중합으로 나타낼 수 있다.Finally, [number 9], which is the probability density function of the cumulative signal at the receiver Rx of FIG. 2,

Can be expressed as a weighted sum of the lognormal distributions as in [13].

이다. here,

to be.

,

은

의 1, 2차 모멘트 매칭을 이용하여 [수 3], [수 4], 및 [수 5]로부터 얻을 수 있다.At this time,

,

silver

Can be obtained from [Numbers 3], [Numbers 4], and [Numerals 5] using the first and second moment matching of

일 때의 평균(

), 분산(

) 은

,

로써, 여기서

,

이다. And,

The average (

), Dispersion(

)

,

Here,

,

to be.

,

는

의 1, 2차 모멘트 매칭을 이용하고,

,

는

의 1, 2차 모멘트 매칭을 이용하여 각각 [수 3], [수 4], 및 [수 5]로부터 얻을 수 있다.Also,

,

The

The first and the second moment matching of the first and second moments are used,

,

The

Can be obtained from [Numbers 3], [Numbers 4], and [Numerals 5] using the first and second moment matching of [

FIG. 4 illustrates a comparison of the performance of statistical models of cumulative signals according to an embodiment of the present invention.

로 두고 여러

값에 대해 두 모델링의 성능을 시뮬레이션 결과와 비교할 수 있다.

As many as

The performance of both modeling can be compared to the simulation results for the values.

=19 (dBm),

= 20,

=1, 2, 5 (m),

=80 (m)로 설정하여

으로 변화시킨다. 이 때,

는 0.9로 설정하고, [수 12]로부터

=1, 2, 5 (m)에 따라 각각

(m) 로 얻을 수 있다. 그리고, 경로 손실 모델로서 IEEE 802.11C 모델이 사용되어

,

, 그리고 쉐도잉 모델의

은 dB 값으로 5 dB로 설정된다. first,

= 19 (dBm),

= 20,

= 1, 2, 5 (m),

= 80 (m)

. At this time,

Is set to 0.9, and from [Numerical Formula 12]

= 1, 2, 5 (m)

(m). The IEEE 802.11 C model is used as the path loss model

,

, And the shadowing model

Is set to 5 dB as a dB value.

Therefore, referring to FIG. 4, the result of the probability density function of the cumulative signal using the parameter set values described above can be confirmed.

에 따라 나타내었다. 각각의

에 대해 비교를 하면,

=16으로 작을 때는 성능 차이가 크게 나지 않으나,

가 40, 80으로 커질수록 기존의 로그 정규 모델은 시뮬레이션과 비교할 때 오차가 매우 커지지만 제안하는 모델은 시뮬레이션 결과와 거의 오차가 발생하지 않음을 확인할 수 있다. 그리고,

=80 일 때

의 개수에 따른 성능을 비교해 볼 때,

=2와 3은 차이가 있지만 3과 4는 거의 동일하기 때문에 [수 11]에서

=3 정도면 충분하다고 판단할 수 있다.
Here, the results of the probability density function using the exact probability density function (black), the log-normal model (blue) and the weighted sum model (red)

Respectively. Each

In comparison,

= 16, the performance difference is not large. However,

Is larger than 40, 80, the error of the logarithmic regular model is much larger than that of the simulation, but it is confirmed that the proposed model has little error with the simulation result. And,

= 80

In the case of performance comparison,

= 2 and 3 are different, but since 3 and 4 are almost the same,

= 3 is enough.

In addition, the statistical modeling system of the radio signal received from the simultaneous transmission of the radio transmitters may comprise a setting section, an area dividing section, a calculating section, and a weighted summing modeling section.

First, the setting unit sets an outermost circle whose radius is a maximum distance by which a signal of a transmitter is affected by a receiver, and an innermost circle whose radius is a minimum distance between the transmitter and the receiver forms a ring-shaped area The radius of the outermost circle, the radius of the innermost circle, and the number of transmitters.

The region dividing unit divides the region into an inner circle existing in the region, divides the region into an outer region including the outermost circle and the inner circle, and an inner region including the innermost circle and the inner circle. have. At this time, the radius (?) Of the inner circle can be obtained, which has been described above, and therefore will not be described.

개의 송신기들이 상기 영역부 내에서 발생할 때,

개가 상기 내부 영역부에 존재하고

개가 상기 외부 영역부 내에 존재할 확률을 이항 분포에 의해 다음 식을 이용하여 구할 수 있다.Also, the calculation unit may obtain a probability density function of the cumulative signal received from the transmitters existing in the outer region unit and the inner region unit, respectively. The calculation unit may perform modeling of a log normal distribution of cumulative signals in each of the outer region and the inner region. And,

When < RTI ID = 0.0 > transmitters < / RTI >

Is present in the inner region portion

The probability that a dog exists in the outer region can be obtained by the binomial distribution using the following equation.

(

)

(

)

This is described above and will be omitted.

Finally, the weighted sum modeling unit can be represented by weighted sum modeling of the lognormal distribution using the probability density function.

Statistical modeling of the cumulative signal proposed in this study can be applied to the development of various technologies that predict the characteristics of signals due to simultaneous transmission of multiple terminals in a dense environment of wireless terminals, which is becoming more frequent in the future. In particular, it can be used for power control of a terminal, system performance analysis and receiver design considering interference that may be caused by simultaneous transmission of terminals in a cognitive radio or an ad hoc network, In the following 3GPP standardization, Rel. 12 environment considers the environment in which small cells are concentrated as a main scenario. Therefore, statistical characteristics of signals simultaneously received from a plurality of small cells can be accurately obtained using the proposed model, It can be used for power control of small cell and development of interference avoidance technique.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, controller, arithmetic logic unit (ALU), digital signal processor, microcomputer, field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing apparatus may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (11)

An outermost circle whose radius is a maximum distance at which a signal of a transmitter is affected by a receiver, and a transmitter distribution area which forms a ring-shaped area with an innermost circle whose radius is a minimum separation distance between the transmitter and the receiver Setting a radius of the outermost circle, a radius of the innermost circle, and a number of the transmitters;
Obtaining a radius of the circle dividing the region into an outer region and an inner region;
Obtaining a probability density function of the cumulative signal received from the transmitters existing in each of the outer region section and the inner region section; And
The weighted sum modeling of the lognormal distribution using the probability density function
The method comprising the steps < RTI ID = 0.0 > of: < / RTI > The method according to claim 1,
Obtaining a probability density function of the cumulative signal received from the transmitters existing in each of the outer region section and the inner region section
Performing modeling of a log normal distribution of cumulative signals in each of the outer region and the inner region;
The method comprising the steps < RTI ID = 0.0 > of: < / RTI > The method according to claim 1,
The step of obtaining the radius of the inner circle dividing the region into the outer region and the inner region
Wherein the radius (?) Of the inner circle is obtained using the following equation,

here,

The radius of the innermost circle,

Is the radius of the outermost circle,
m is the number of transmitters, and P ₀ = 0.85 to 0.95
The method comprising the steps < RTI ID = 0.0 > of: < / RTI > The method according to claim 1,
Obtaining a probability density function of an accumulated signal received from the transmitters existing in each of the outer region section and the inner region section,

When < RTI ID = 0.0 > transmitters < / RTI >

Is present in the inner region portion

By using a binomial distribution, using the following equation: < EMI ID =

here,

The radius of the innermost circle,

The radius of the outermost circle, and

Represents the radius of the inner circle,
m is the number of transmitters, i is the number of transmitters in the inner region, and mi is the number of transmitters in the outer region,

Is a probability that one of the transmitters generated in the area unit exists in the inner area unit,

Is a probability that one of the transmitters generated in the area unit exists in the outer area unit,

Thing
The method comprising the steps < RTI ID = 0.0 > of: < / RTI > 5. The method of claim 4,
Obtaining a probability density function of an accumulated signal received from the transmitters existing in each of the outer region section and the inner region section,
The smallest

Value,

here,

Is set to at least one of 0.01 and 0.001 according to the approximation accuracy requirement,

Has an integer value between 2 and 4
The method comprising the steps < RTI ID = 0.0 > of: < / RTI > The method according to claim 1,
The step of weighted sum modeling of the lognormal distribution using the probability density function
A weighted sum modeling of the lognormal distribution using a probability density function of the inner region and a convolution of a probability density function of the outer region;
≪ RTI ID = 0.0 > 1, < / RTI > 6. The method of claim 5,
The step of weighted sum modeling of the lognormal distribution using the probability density function
Average(

) And variance (

), And calculating a weighted sum of the lognormal distributions using the following equation,

here,

Thing
The method comprising the steps < RTI ID = 0.0 > of: < / RTI > An outermost circle whose radius is a maximum distance at which a signal of a transmitter is affected by a receiver, and a transmitter distribution area which forms a ring-shaped area with an innermost circle whose radius is a minimum separation distance between the transmitter and the receiver A setting unit for setting a radius of the outermost circle, a radius of the innermost circle, and a number of the transmitters;
An area dividing section dividing the area section into an inner circle existing in the area section and dividing the area section into an outer area section including the outermost circle and the inner circle and an inner area section including the innermost circle circle and the inner circle;
A calculation unit for obtaining a probability density function of the cumulative signal received from the transmitters existing in each of the outer area unit and the inner area unit; And
A weighted sum modeling unit that represents weighted sum modeling of the log normal distribution using the probability density function,
Characterized in that the system further comprises a transmitter for receiving the radio signals from the transmitter. 9. The method of claim 8,
The calculation unit
Performing modeling of the log normal distribution of the cumulative signal in each of the outer region and the inner region
Characterized in that the radio signal is received by the transmitter. 9. The method of claim 8,
In the region dividing section
The radius (?) Of the inner circle is obtained by using the following equation,

here,

The radius of the innermost circle,

Is the radius of the outermost circle,
m is the number of transmitters, and P ₀ = 0.85 to 0.95
Characterized in that the radio signal is received by the transmitter. 9. The method of claim 8,
The calculation unit

When < RTI ID = 0.0 > transmitters < / RTI >

Is present in the inner region portion

The probability that the dog exists in the outer region is obtained by the binomial distribution using the following equation,

here,

The radius of the innermost circle,

The radius of the outermost circle, and

Represents the radius of the inner circle,
m is the number of transmitters, i is the number of transmitters in the inner region, and mi is the number of transmitters in the outer region,

Is a probability that one of the transmitters generated in the area unit exists in the inner area unit,

Is a probability that one of the transmitters generated in the area unit exists in the outer area unit,

Thing
Characterized in that the radio signal is received by the transmitter.

Images